Title :
Fuzzy Clustering by Differential Evolution
Author :
Kao, Yucheng ; Lin, Jin-Cherng ; Huang, Shin-Chia
Author_Institution :
Dept. of Inf. Manage., Tatung Univ., Taipei
Abstract :
A fuzzy clustering algorithm based on differential evolution (FCDE) is presented in this paper in order to overcome the disadvantages of traditional fuzzy c-means algorithm (FCM). FCM is sensitive to initialization so that its search is easy to fall into a local optimum. The algorithm we proposed in this paper will avoid this problem and lead to global optimum. The experiments show that FCDE has better performance than FCM and is more efficient particularly when the number of dimension of data becomes large.
Keywords :
evolutionary computation; fuzzy set theory; pattern clustering; differential evolution; fuzzy c-means algorithm; fuzzy clustering; Application software; Clustering algorithms; Clustering methods; Computer science; Data mining; Design engineering; Fuzzy systems; Genetic algorithms; Information management; Intelligent systems; Data Clustering; Differential Evolution; Fuzzy c-Means;
Conference_Titel :
Intelligent Systems Design and Applications, 2008. ISDA '08. Eighth International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-0-7695-3382-7
DOI :
10.1109/ISDA.2008.270
